Apparatus for determining a most limiting parameter of an electric aircraft

ABSTRACT

In an aspect an apparatus for determining a most limiting parameter of an electric aircraft is presented. An apparatus includes at least a processor and a memory communicatively connected to the at least a processor. A memory contains instructions configuring at least a processor to receive aircraft data from a sensing device. A sensing device is configured to measure a parameter of an electric aircraft and generate aircraft data. At least a processor is configured to determine a most limiting parameter of an electric aircraft as a function of aircraft data. At least a processor is configured to communicate a most limiting parameter to a pilot indicator in communication with the at least a processor and memory communicatively connected to the at least a processor. A pilot indicator is configured to display a most limiting parameter to a user.

FIELD OF THE INVENTION

The present invention generally relates to the field of electricaircraft. In particular, the present invention is directed to anapparatus for determining a most limiting parameter of an electricaircraft.

BACKGROUND

Electric aircraft have many parts and systems that use up fuel andenergy stores during operation. However, modern electric aircraft do nothave an apparatus that determines a most limiting parameter of all partsand systems used by electric aircraft during flight.

SUMMARY OF THE DISCLOSURE

In an aspect an apparatus for determining a most limiting parameter ofan electric aircraft is presented. An apparatus includes at least aprocessor and a memory communicatively connected to the at least aprocessor. A memory contains instructions configuring at least aprocessor to receive aircraft data from a sensing device. A sensingdevice is configured to measure a parameter of an electric aircraft andgenerate aircraft data. At least a processor is configured to determinea most limiting parameter of an electric aircraft as a function ofaircraft data. A most limiting parameter is related to at least abattery of the electric aircraft. At least a processor is configured tocommunicate a most limiting parameter to a pilot indicator incommunication with the at least a processor and memory communicativelyconnected to the at least a processor. A pilot indicator is configuredto display a most limiting parameter to a user.

In another aspect a method of determining a most limiting parameter ofan electric aircraft is presented. A method includes measuring at asensing device of an electric aircraft a parameter of the electricaircraft. A method includes generating at a sensing device aircraft dataas a function of a parameter of an electric aircraft. A method includescommunicating aircraft data from a sensing device to at least aprocessor and a memory communicatively connected to the at least aprocessor. A method includes determining a most limiting parameter of anelectric aircraft as a function of aircraft data by at least a processorand a memory communicatively connected to the at least a processor. Amethod includes communicating a most limiting parameter of an electricaircraft from at least a processor and a memory communicativelyconnected to the at least a processor to a pilot indicator. A methodincludes displaying a most limiting parameter to a user.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is an exemplary embodiment of an apparatus for determining a mostlimiting parameter of an electric aircraft;

FIG. 2 is an exemplary embodiment of an electric aircraft;

FIG. 3 is an exemplary embodiment of a flight controller;

FIG. 4 is an exemplary embodiment of a sensor suite;

FIG. 5 is an exemplary embodiment of an indicator database;

FIG. 6 is a flowchart of a method of determining a most limitingparameter;

FIG. 7 is a block diagram of a machine learning model; and

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed to anapparatus for determining a most limiting parameter of an electricaircraft. In an embodiment, an apparatus may include at least aprocessor and a memory communicatively connected to the at least aprocessor. An apparatus may be configured to receive aircraft data froma sensing device of an electric aircraft and determine compare theaircraft data to flight confidence parameters to determine a mostlimiting parameter. A most limiting parameter may include a flightparameter of an electric aircraft that may reduce a flight range of theelectric aircraft.

Aspects of the present disclosure can be used to predict most limitingparameters of flights of electric aircraft using a most limitingparameter machine learning model. Aspects of the present disclosure canalso be used to alert a user, such as a pilot, to a flight parameterexceeding an operational threshold. This is so, at least in part, apilot may be alerted of a flight parameter that may limit a flightrange.

Aspects of the present disclosure allow for informed flight planning andanalytics. A most limiting parameter may assist in determining flightpaths, generating power saving flight plans, and the like. Exemplaryembodiments illustrating aspects of the present disclosure are describedbelow in the context of several specific examples.

Referring now to FIG. 1 , an exemplary embodiment of an apparatus 100for determining a most limiting parameter is illustrated. Apparatus 100may include a computing device. A computing device may include anycomputing device as described in this disclosure, including withoutlimitation a microcontroller, microprocessor, digital signal processor(DSP) and/or system on a chip (SoC) as described in this disclosure. Acomputing device may include, be included in, and/or communicate with amobile device such as a mobile telephone or smartphone. Apparatus 100may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. Apparatus 100 may interface or communicate with one or moreadditional devices as described below in further detail via a networkinterface device. Network interface device may be utilized forconnecting apparatus 100 to one or more of a variety of networks, andone or more devices. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer and/or a computingdevice. Apparatus 100 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Apparatus 100 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Apparatus 100 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of apparatus 100, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Apparatus 100 may be implemented usinga “shared nothing” architecture in which data is cached at the worker,in an embodiment, this may enable scalability of apparatus 100.

With continued reference to FIG. 1 , apparatus 100 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, apparatus 100 maybe configured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Apparatus 100 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Still referring to FIG. 1 , apparatus 100 may include at least aprocessor 104 and a memory 108 communicatively connected to the at leasta processor 104. “Communicatively connected” as used in this disclosureis an attribute of a connection, attachment or linkage between two ormore relata which allows for reception and/or transmittance ofinformation therebetween. In some embodiments, apparatus 100 may includea flight controller as described below with reference to FIG. 3 . Insome embodiments, memory 108 may include instructions that may configurethe at least a processor 104 to perform various tasks. Instructions maybe received from, but not limited to, an external computing device, userinput, and the like. Apparatus 100 may be communicatively connected withsensing device 112. A “sensing device” as used in this disclosure is adevice that is configured to detect a phenomenon and transmitinformation related to the detection of the phenomenon. Sensing device112 may be configured to transduce a detected phenomenon, such aswithout limitation, voltage, current, speed, direction, force, torque,temperature, pressure, and the like, into a sensed signal. In one ormore embodiments, sensing device 112 may include a plurality of sensors.Sensing device 112 may include, but is not limited to, an electricalsensor, an imaging sensor, such as a camera or infrared sensor, a motionsensor, an inertia measurement unit (IMU), a radio frequency sensor, alight detection and ranging (LIDAR) sensor, an orientation sensor, atemperature sensor, a humidity sensor, or the like, as discussed furtherbelow in this disclosure. In one or more embodiments, and withoutlimitation, sensing device 112 may include one or more temperaturesensors, voltmeters, current sensors, hydrometers, infrared sensors,photoelectric sensors, ionization smoke sensors, motion sensors,pressure sensors, radiation sensors, level sensors, imaging devices,moisture sensors, gas and chemical sensors, flame sensors, electricalsensors, imaging sensors, force sensors, Hall sensors, and the like.Sensing device 112 may include a contact or a non-contact sensor.Sensing device 112 may include one or more sensors which may be thesame, similar or different. Sensing device 112 may include a pluralityof sensors which may be the same, similar or different. Sensing device112 may include one or more sensor suites with sensors in each sensorsuite being the same, similar or different. A sensor suite may be asdescribed below with reference to FIG. 4 . Still referring to FIG. 1 ,sensing device 112 may be configured to generate aircraft data 116 as afunction of one or more detected phenomenon. “Aircraft data” as used inthis disclosure is information pertaining to one or more parts,components, or systems of an aircraft. Aircraft data 116 may include,but is not limited to, battery temperature, battery health, batterycharge, aircraft altitude, aircraft velocity, aircraft acceleration,rotor torque, aircraft power systems, and the like. Sensing device 112may be in communicative connection with a propulsor 136. For thepurposes of this disclosure, a “propulsor” is a component or device usedto propel a craft by exerting force on a fluid medium, which may includea gaseous medium such as air or a liquid medium such as water. Propulsor136 may include any device or component that consumes electrical poweron demand to propel an electric aircraft in a direction while on groundor in-flight. For example, and without limitation, propulsor may includea rotor, propeller, paddle wheel, and the like thereof. In anembodiment, propulsor may include a plurality of blades that radiallyextend from a hub of the propulsor so that the blades may convert arotary motion from a motor into a swirling slipstream. In an embodiment,blade may convert rotary motion to push an aircraft forward or backward.For instance, and without limitation, propulsor 136 may include anassembly including a rotating power-driven hub, to which severalradially-extending airfoil-section blades are fixedly attached thereto,where the whole assembly rotates about a central longitudinal axis A.The blade pitch of a propeller may, for example, be fixed, manuallyvariable to a few set positions, automatically variable (e.g., a“constant-speed” type), or any combination thereof. In an exemplaryembodiment, propellers for an aircraft may be designed to be fixed totheir hub at an angle similar to the thread on a screw makes an angle tothe shaft; this angle may be referred to as a pitch or pitch angle whichwill determine the speed of the forward movement as the blade rotates.In one or more exemplary embodiments, propulsor 136 may include avertical propulsor or a forward propulsor. A forward propulsor mayinclude a propulsor configured to propel aircraft 104 in a forwarddirection. A vertical propulsor may include a propulsor configured topropel aircraft 104 in an upward direction. One of ordinary skill in theart would understand upward to comprise the imaginary axis protrudingfrom the earth at a normal angle, configured to be normal to any tangentplane to a point on a sphere (i.e. skyward). In an embodiment, verticalpropulsor can be a propulsor that generates a substantially downwardthrust, tending to propel an aircraft in an opposite, vertical directionand provides thrust for maneuvers. Such maneuvers can include, withoutlimitation, vertical take-off, vertical landing, hovering, and/orrotor-based flight such as “quadcopter” or similar styles of flight.

In one or more embodiments, propulsor 136 can include a thrust elementwhich may be integrated into the propulsor. The thrust element mayinclude, without limitation, a device using moving or rotating foils,such as one or more rotors, an airscrew, or propeller, a set ofairscrews or propellers such as contra-rotating propellers, a moving orflapping wing, or the like. Further, a thrust element, for example, caninclude without limitation a marine propeller or screw, an impeller, aturbine, a pump-jet, a paddle or paddle-based device, or the like. Inone or more embodiments, propulsor 136 may include a pusher component.As used in this disclosure a “pusher component” is a component thatpushes and/or thrusts an aircraft through a medium. As a non-limitingexample, pusher component may include a pusher propeller, a paddlewheel, a pusher motor, a pusher propulsor, and the like. Pushercomponent may be configured to produce a forward thrust. As used in thisdisclosure a “forward thrust” is a thrust that forces aircraft through amedium in a horizontal direction, wherein a horizontal direction is adirection parallel to the longitudinal axis. For example, forward thrustmay include a force of 1145 N to force electric aircraft 104 in ahorizontal direction along a longitudinal axis of electric aircraft 104.As a further non-limiting example, pusher component may twist and/orrotate to pull air behind it and, at the same time, push electricaircraft 104 forward with an equal amount of force. In an embodiment,and without limitation, the more air forced behind aircraft, the greaterthe thrust force with which electric aircraft 104 is pushed horizontallywill be. In another embodiment, and without limitation, forward thrustmay force electric aircraft 104 through the medium of relative air.Additionally or alternatively, plurality of propulsor may include one ormore puller components. As used in this disclosure a “puller component”is a component that pulls and/or tows an aircraft through a medium. As anon-limiting example, puller component may include a flight componentsuch as a puller propeller, a puller motor, a tractor propeller, apuller propulsor, and the like. Additionally, or alternatively, pullercomponent may include a plurality of puller flight components. Propulsor136 may include, but is not limited to, a motor, rotor, stator, blades,inverters, and the like. Propulsor 136 may be as described below withreference to FIG. 2 . Sensing device 132 may detect and/or measurevalues of propulsor 136, such as, but not limited to, rotational speeds,torques, voltages, currents, temperatures, and the like. Sensing device112 may be in communicative connection with battery pack 140. Batterypack 140 may include one or more battery cells, such as, but not limitedto, lithium-ion cells, pouch cells, and the like. Battery pack 140 maybe configured to power electric aircraft 132. Battery pack 140 may be asdescribed below with reference to FIG. 4 . Sensing device 112 may detectand/or measure values of battery pack 140, such as, but not limited to,state of charge, capacity, temperature, voltages, currents, resistances,and the like. In some embodiments, apparatus 100 may be configured toreceive aircraft data 116 from sensing device 112. Aircraft data 116 maybe received wirelessly and/or wired from sensing device 112 at apparatus100. Apparatus 100 may be configured to compare aircraft data 116 toflight confidence parameter 120. A “flight confidence parameter” as usedin this disclosure is a parameter to which aircraft data can be comparedto determine a degree of readiness for a given flight and/or for flightin general. Flight confidence parameter 120 may include a minimumthreshold. A “minimum threshold” as used in this disclosure is a metricvalue limit. As a non-limiting example, a minimum threshold may includea state of charge of battery pack 140. In some embodiments, flightconfidence parameter 120 may include a maximum value of a flightparameter. For instance, and without limitation, a maximum value mayinclude battery temperatures, torque output, altitudes, hover times,cruising times, propulsor temperature, and the like. Flight confidenceparameter 120 may include an optimal flight parameter. An “optimalflight parameter” as used in this disclosure is an ideal performancemetric of one or more parts or systems of an aircraft. Flight confidenceparameter 120 may include, but is not limited to, torques, speeds,altitudes, power outputs, and the like. As a non-limiting example,flight confidence parameter 120 may include a torque of a front rotor ofan electric aircraft of 300 N*m during a cruising phase of the electricaircraft. Flight confidence parameter 120 may be specific to givenflight paths, flight missions, aircraft type, and the like. For instanceand without limitation, a flight mission may include a short rangeflight of about 50 miles. Flight confidence parameter 120 may have ahigher minimum threshold of battery charge of battery pack 136 of about60%. Flight confidence parameter 120 may be received from an externalcomputing device and/or from user input.

Still referring to FIG. 1 , in some embodiments, flight confidenceparameter 120 may include one or more parameters as described in U.S.patent application Ser. No. 17/349,182, filed Jun. 16, 2021, and titled“SYSTEMS AND METHODS FOR IN-FLIGHT OPERATIONAL ASSESSMENT”, which isincorporated by reference herein in its entirety.

Still referring to FIG. 1 , in some embodiments, apparatus 100 maydetermine flight confidence parameter 120 as a function of aircraft datafrom a hybrid vehicle. Determining flight confidence parameter 120 for ahybrid vehicle may be as described in U.S. patent application Ser. No.17/734,015, filed Apr. 30, 2022, and titled “SYSTEM FOR AN INTEGRALHYBRID ELECTRIC AIRCRAFT”, U.S. patent application Ser. No. 17/734,014,filed Apr. 30, 2022, and titled “HYBRID PROPULSION SYSTEMS FOR ANELECTRIC AIRCRAFT”, and U.S. patent application Ser. No. 17/733,487,filed Apr. 29, 2022, and titled “HYBRID ELECTRIC VERTICAL TAKEOFF ANDLANDING AIRCRAFT”, each of which is incorporated by reference herein intheir entirety. Still referring to FIG. 1 , apparatus 100 may compareaircraft data 116 to flight confidence parameter 120 using anoptimization criterion. An “optimization criterion” as used in thisdisclosure is a value that is sought to be maximized or minimized in asystem. Apparatus 100 may use an objective function to compare aircraftdata 116 to flight confidence parameter 120. An “objective function” asused in this disclosure is a process of minimizing or maximizing one ormore values based on a set of constraints. Apparatus 100 may generate anobjective function to optimize a flight plan of an electric aircraft. Insome embodiments, an objective function of apparatus 100 may include anoptimization criterion. An optimization criterion may include anydescription of a desired value or range of values for one or moreattributes of a flight; desired value or range of values may include amaximal or minimal value, a range between maximal or minimal values, oran instruction to maximize or minimize an attribute. As a non-limitingexample, an optimization criterion may specify that a flight speed of anelectric aircraft should be at least 200 MPH; an optimization criterionmay cap a flight speed of an electric aircraft, for instance specifyingthat an electric aircraft must not have a speed greater than a specifiedvalue. An optimization criterion may alternatively request that a flightparameter be greater than a certain value. An optimization criterion mayspecify one or more tolerances for fuel usage of a flight. Anoptimization criterion may specify one or more desired flight parametersof an electric aircraft, such as, but not limited to, torque speed,battery temperatures, battery health, altitudes, and the like. In anembodiment, an optimization criterion may assign weights to differentattributes or values associated with attributes; weights, as usedherein, may be multipliers or other scalar numbers reflecting a relativeimportance of a particular attribute or value. One or more weights maybe expressions of value to a user of a particular outcome, attributevalue, or other facet of a flight parameter; value may be expressed, asa non-limiting example, in remunerative form, such as a fuel cost, aquickest flight time, and the like. As a non-limiting example,minimization of flight time may be multiplied by a first weight, whiletolerance above a certain value may be multiplied by a second weight.Optimization criteria may be combined in weighted or unweightedcombinations into a function reflecting an overall outcome desired by auser; function may be a fuel function to be minimized and/or maximized.Function may be defined by reference to flight parameter constraintsand/or weighted aggregation thereof as provided by apparatus 100; forinstance, a fuel function combining optimization criteria may seek tominimize or maximize a function of battery temperature.

Still referring to FIG. 1 , apparatus 100 may use an objective functionto compare aircraft data 116 with flight confidence parameter 120.Generation of an objective function may include generation of a functionto score and weight factors to achieve a process score for each feasiblepairing. In some embodiments, pairings may be scored in a matrix foroptimization, where columns represent aircraft data and rows representflight confidence parameters potentially paired therewith; each cell ofsuch a matrix may represent a score of a pairing of the correspondingaircraft data to the corresponding flight confidence parameter. In someembodiments, assigning a predicted process that optimizes the objectivefunction includes performing a greedy algorithm process. A “greedyalgorithm” is defined as an algorithm that selects locally optimalchoices, which may or may not generate a globally optimal solution. Forinstance, apparatus 100 may select pairings so that scores associatedtherewith are the best score for each order and/or for each flightparameter. In such an example, optimization may determine thecombination of flight parameters such that each object pairing includesthe highest score possible.

Still referring to FIG. 1 , an objective function may be formulated as alinear objective function. Apparatus 100 may solve an objective functionusing a linear program such as without limitation a mixed-integerprogram. A “linear program,” as used in this disclosure, is a programthat optimizes a linear objective function, given at least a constraint.For instance, and without limitation, objective function may seek tomaximize a total score Σ_(rϵR) ΣsϵS c_(rs)x_(rs), where R is a set ofall aircraft data r, S is a set of all flight confidence parameters s,c_(rs) is a score of a pairing of a given aircraft datum with a givenmatch, and x_(rs) is 1 if an aircraft datum r is paired with a flightconfidence parameter s, and 0 otherwise. Continuing the example,constraints may specify that each aircraft datum is assigned to only oneflight confidence parameter, and each flight confidence parameter isassigned only one aircraft datum. Flight confidence parameters mayinclude flight confidence parameters as described above. Sets of flightconfidence parameters may be optimized for a maximum score combinationof all generated flight confidence parameters. In various embodiments,apparatus 100 may determine a combination of flight confidenceparameters that maximizes a total score subject to a constraint that allaircraft data are paired to exactly one flight confidence parameter. Notall flight confidence parameters may receive an aircraft datum pairingsince each flight confidence parameter may only produce one aircraftdatum. In some embodiments, an objective function may be formulated as amixed integer optimization function. A “mixed integer optimization” asused in this disclosure is a program in which some or all of thevariables are restricted to be integers. A mathematical solver may beimplemented to solve for the set of feasible pairings that maximizes thesum of scores across all pairings; mathematical solver may beimplemented on processor 104 of apparatus 100 and/or another device ofapparatus 100, and/or may be implemented on third-party solver.

With continued reference to FIG. 1 , optimizing an objective functionmay include minimizing a loss function, where a “loss function” is anexpression an output of which an optimization algorithm minimizes togenerate an optimal result. As a non-limiting example, apparatus 100 mayassign variables relating to a set of parameters, which may correspondto score flight parameters as described above, calculate an output ofmathematical expression using the variables, and select a pairing thatproduces an output having the lowest size, according to a givendefinition of “size,” of the set of outputs representing each ofplurality of candidate ingredient combinations; size may, for instance,included absolute value, numerical size, or the like. Selection ofdifferent loss functions may result in identification of differentpotential pairings as generating minimal outputs. Objectives representedin an objective function and/or loss function may include minimizationof fuel use. Objectives may include minimization of batterytemperatures. Objectives may include minimization of flight times.Objectives may include minimization of differences between aircraft data116 and flight confidence parameter 120.

Still referring to FIG. 1 , apparatus 100 may determine most limitingparameter 124. A “most limiting parameter” as used in this disclosure isan element of data associated with a part or system of an electricaircraft that has a most significant effect on a flight range of theelectric aircraft. Apparatus 100 may generate a list of potential mostlimiting parameters. A list of potential most limiting parameters mayinclude flight parameters related to heating systems. Heating systemsmay include AC systems that increase or decrease a temperature of partsof electric aircraft 132 such as a cockpit, passenger area, cargo area,and the like. A list of potential most limiting parameters may includeflight parameters such as rotor torque. One or more rotors of electricaircraft 132 may rotate, producing a torque to assist in a maneuveringof electric aircraft 132. One or more rotors of electric aircraft 132may require energy, such as electricity, to operate. A high torque ofone or more rotors of electric aircraft 132 may drain a fuel source ofelectric aircraft 132, such as a battery pack and/or battery cells. Alist of potential most limiting parameters may include aircraft speed. Aspeed of electric aircraft 132 may determine an amount of energy drainedfrom a battery pack and/or battery cells of electric aircraft 132. Afaster speed of electric aircraft 132 may drain a battery pack and/orbattery cells of electric aircraft 132 more rapidly than, for example, acruising speed. A list of potential most limiting parameters may includeparameters related to one or more flight modes. A “flight mode” as usedin this disclosure is a type of flight an electric aircraft engages in.Flight modes may include, but are not limited to, takeoff, hovering,climbing, cruising, descent, approach, hovering, landing, and anytransition between thereof. As a non-limiting example, a flight mode oftakeoff may drain a battery pack and/or battery cells of an electricaircraft faster than a cruising flight mode. Likewise, a hovering flightmode may require more energy than a descent flight mode, which may beattributed to a reduced rotor speed. In some embodiments, most limitingparameter 124 may be related to at least a battery of electric aircraft132, such as battery pack 140. Most limiting parameter 124 may includeparameters related to battery health. Battery health may include, but isnot limited to, reduced capacity, full capacity, overheating,overcooling, and the like. As a non-limiting example, a colder batterymay see reduced performance due to slower electrochemical reactionswithin the battery. Likewise, an overheated battery may see reducedperformance due to excessive electrochemical reactions. Most limitingparameter 124 may include a state of charge of a battery. A state ofcharge may include a percent charge of a battery and/or battery pack. Apercent charge may include, but is not limited to, 20%, 40%, 60%, 80%,and the like. A reduced state of charge may reduce a flight range ofelectric aircraft 132. In some embodiments, a list of potential mostlimiting parameters may include parameters related to inverter health.An “inverter” as used in this disclosure is an electric componentconfigured to transform direct current (DC) to alternating current (AC).An overheated or overcooled inverter may see reduced performance. A listof potential most limiting parameters may include inverter, rotor,and/or motor temperature. Like inverters and batteries, rotors andmotors may be prone to reduced performance due to overheating orovercooling. Apparatus 100 may determine most limiting parameter 124from a list of potential most limiting parameters. As a non-limitingexample, a list of potential most limiting parameters may show ACsystems reduce flight range by 3%, a higher altitude reduces flightrange of 8%, and high inverter temperatures reduce flight range of 14%.Apparatus 100 may determine high inverter temperatures to be mostlimiting parameter 124. In some embodiments, apparatus 100 may utilize amost limiting parameter machine learning model. A most limitingparameter machine learning model may be trained on training datacorrelating aircraft data to most limiting parameters. Training data maybe received from user input, external computing devices, and/or previousiterations of processing. Apparatus 100 may use a most limitingparameter machine learning model to determine most limiting parameter124.

Still referring to FIG. 1 , in some embodiments, apparatus 100 maydetermine most limiting parameter 124 as a function of an operationalthreshold. An “operational threshold” as used in this disclosure is avalue that if reached triggers a change in a system. An operationalthreshold may include, but is not limited to, fuel use, temperatures,power output, torques, and the like. As a non-limiting example,apparatus 100 may compare aircraft data 116 to an operational thresholdof a voltage of 250V of a rotor. Aircraft data 116 may show that a rotormay be operating at 267V, which may prompt apparatus 100 to determinethe rotor to be most limiting parameter 124. In some embodiments,apparatus 100 may communicate most limiting parameter 124 to pilotindicator 128. A “pilot indicator” as used in this disclosure is adevice capable of conveying aircraft information to a user. In somecases, a pilot indicator may include a most limiting parameter 124.Pilot indicator 128 may include, but is not limited to, light emittingdiode (LED) displays, liquid crystal displays (LCD), and the like. Pilotindicator 128 may include alarms such as, but not limited to, visualalarms, audio alarms, and the like. Apparatus 100 may alarm a userthrough pilot indicator 128 as a function of a reached operationalthreshold. As a non-limiting example, an operational threshold mayinclude an altitude of 30,000 ft. Aircraft data 116 may show that anelectric aircraft is flying at an altitude of 34,000 ft. Apparatus 100may alert a user of at least a part of a flight system of an electricaircraft that exceeds an operational threshold, such as torque output ofone or more motors resulting in excessive altitude. An alert that may bedisplayed and/or sounded to a user through pilot indicator 128.

Still referring to FIG. 1 , apparatus 100 may generate a power savingflight plan as a function of a comparison of aircraft data 116 to flightconfidence parameter 120. A “power saving flight plan” as used in thisdisclosure is a set of actions that minimizes power usage of anaircraft. A power saving flight plan may include recommendations thatmay be displayed through pilot indicator 128, such as, but not limitedto, turning off lighting systems, turning off AC systems, reducingspeed, adjusting flight paths, adjusting flight modes, and the like. Asa non-limiting example, a power saving flight plan may include turningoff cargo lights, reducing altitude, reducing speed, and taking a moredirect route to a destination. Apparatus 100 may be configured toautomatically engage in a power saving plan. As a non-limiting example,apparatus 100 may determine electric aircraft 132 is at 30% fuelremaining. Apparatus 100 may automatically reduce high energy consumingoperations of electric aircraft 132. In some embodiments, apparatus 100may present a power saving flight plan to a user through pilot indicator128. A user may accept or reject a power saving flight plan. In someembodiments, apparatus 100 may utilize a power saving flight planmachine learning model. A power saving flight plan machine learningmodel may be trained on training data correlating aircraft data toenergy saving actions. Training data may be received from user input,external computing devices, and/or previous iterations of processing.Apparatus 100 may determine a power saving flight plan as a function ofan output of a power saving flight plan machine learning model.

Still referring to FIG. 1 , apparatus 100 may generate a mitigatingresponse. A “mitigating response” as used in this disclosure is anaction that reduces a parameter. Apparatus 100 may generate a mitigatingresponse as a function of most limiting parameter 124. For instance andwithout limitation, most limiting parameter 124 may show battery pack136 is overheating. Apparatus 100 may generate a mitigating response ofreducing speeds, torques, and the like of electric aircraft 132. Asanother non-limiting example, apparatus 100 may determine most limitingparameter 124 includes an inverter failure. Apparatus 100 may determinea mitigating response includes a switching of a flight mode from cruiseand/or fixed wing landings. Apparatus 100 may present a mitigatingresponse to a user, such as through pilot indicator 128. A user mayaccept a mitigating response, and apparatus 100 may perform themitigating response. Apparatus 100 may determine a second most limitingparameter and determine a mitigating response of the second mostlimiting parameter. Apparatus 100 may present a mitigating response of asecond most limiting parameter to a user through pilot indicator 128. Aprocess of determining subsequent most limiting parameters andcorresponding mitigating responses may be repeated indefinitely, untilno most limiting parameter is found, until a user rejects a mitigatingresponse, and/or until no mitigating response is found. Apparatus 100may utilize a mitigating response machine learning model. A mitigatingresponse machine learning model may be trained with training datacorrelating aircraft data to most limiting parameters and mitigatingresponses. Training data may be received from user input, remotecomputing devices, and/or previous iterations of processing. Amitigating response machine learning model may be configured to inputaircraft data and output most limiting parameters and mitigatingresponses. Apparatus 100 may determine subsequent most limitingparameters and/or mitigation response as a function of a mitigationresponse machine learning model.

Referring now to FIG. 2 , an exemplary embodiment of an electricaircraft 200 is illustrated. Electric aircraft 200, and any of itsfeatures, may be used in conjunction with any of the embodiments of thepresent disclosure. Electric aircraft 200 may include any of theaircrafts as disclosed herein including electric aircraft 132 of FIG. 1. In an embodiment, electric aircraft 200 may be an electric verticaltakeoff and landing (eVTOL) aircraft. As used in this disclosure, an“aircraft” is any vehicle that may fly by gaining support from the air.As a non-limiting example, aircraft may include airplanes, helicopters,commercial, personal and/or recreational aircrafts, instrument flightaircrafts, drones, electric aircrafts, airliners, rotorcrafts, verticaltakeoff and landing aircrafts, jets, airships, blimps, gliders,paramotors, quad-copters, unmanned aerial vehicles (UAVs) and the like.As used in this disclosure, an “electric aircraft” is an electricallypowered aircraft such as one powered by one or more electric motors orthe like. In some embodiments, electrically powered (or electric)aircraft may be an electric vertical takeoff and landing (eVTOL)aircraft. Electric aircraft 200 may be capable of rotor-based cruisingflight, rotor-based takeoff, rotor-based landing, fixed-wing cruisingflight, airplane-style takeoff, airplane-style landing, and/or anycombination thereof. Electric aircraft 200 may include one or moremanned and/or unmanned aircrafts. Electric aircraft 200 may include oneor more all-electric short takeoff and landing (eSTOL) aircrafts. Forexample, and without limitation, eSTOL aircrafts may accelerate theplane to a flight speed on takeoff and decelerate the plane afterlanding. In an embodiment, and without limitation, electric aircraft maybe configured with an electric propulsion assembly. Including one ormore propulsion and/or flight components. Electric propulsion assemblymay include any electric propulsion assembly (or system) as described inU.S. Nonprovisional application Ser. No. 16/703,225, filed on Dec. 4,2019, and entitled “AN INTEGRATED ELECTRIC PROPULSION ASSEMBLY,” theentirety of which is incorporated herein by reference.

Still referring to FIG. 2 , as used in this disclosure, a “verticaltake-off and landing (VTOL) aircraft” is one that can hover, take off,and land vertically. An “electric vertical takeoff and landing aircraft”or “eVTOL aircraft”, as used in this disclosure, is an electricallypowered aircraft typically using an energy source, of a plurality ofenergy sources to power the aircraft. In order to optimize the power andenergy necessary to propel the aircraft, eVTOL may be capable ofrotor-based cruising flight, rotor-based takeoff, rotor-based landing,fixed-wing cruising flight, airplane-style takeoff, airplane stylelanding, and/or any combination thereof. Rotor-based flight, asdescribed herein, is where the aircraft generates lift and propulsion byway of one or more powered rotors or blades coupled with an engine, suchas a “quad copter,” multi-rotor helicopter, or other vehicle thatmaintains its lift primarily using downward thrusting propulsors.“Fixed-wing flight”, as described herein, is where the aircraft iscapable of flight using wings and/or foils that generate lift caused bythe aircraft's forward airspeed and the shape of the wings and/or foils,such as airplane-style flight.

Still referring to FIG. 2 , electric aircraft 200, in some embodiments,may generally include a fuselage 204, a flight component 208 (or aplurality of flight components 208), a pilot control 220, an aircraftsensor 228 (or a plurality of aircraft sensors 228) and flightcontroller 124. In one embodiment, flight components 208 may include atleast a lift component 212 (or a plurality of lift components 212) andat least a pusher component 216 (or a plurality of pusher components216). Aircraft sensor(s) 228 may be the same as or similar to aircraftsensor(s) 160 of FIG. 1 .

Still referring to FIG. 2 , as used in this disclosure a “fuselage” isthe main body of an aircraft, or in other words, the entirety of theaircraft except for the cockpit, nose, wings, empennage, nacelles, anyand all control surfaces, and generally contains an aircraft's payload.Fuselage 204 may include structural elements that physically support ashape and structure of an aircraft. Structural elements may take aplurality of forms, alone or in combination with other types. Structuralelements may vary depending on a construction type of aircraft such aswithout limitation a fuselage 204. Fuselage 204 may comprise a trussstructure. A truss structure may be used with a lightweight aircraft andcomprises welded steel tube trusses. A “truss,” as used in thisdisclosure, is an assembly of beams that create a rigid structure, oftenin combinations of triangles to create three-dimensional shapes. A trussstructure may alternatively comprise wood construction in place of steeltubes, or a combination thereof. In embodiments, structural elements maycomprise steel tubes and/or wood beams. In an embodiment, and withoutlimitation, structural elements may include an aircraft skin. Aircraftskin may be layered over the body shape constructed by trusses. Aircraftskin may comprise a plurality of materials such as plywood sheets,aluminum, fiberglass, and/or carbon fiber.

Still referring to FIG. 2 , it should be noted that an illustrativeembodiment is presented only, and this disclosure in no way limits theform or construction method of any of the aircrafts as disclosed herein.In embodiments, fuselage 204 may be configurable based on the needs ofthe aircraft per specific mission or objective. The general arrangementof components, structural elements, and hardware associated with storingand/or moving a payload may be added or removed from fuselage 204 asneeded, whether it is stowed manually, automatedly, or removed bypersonnel altogether. Fuselage 204 may be configurable for a pluralityof storage options. Bulkheads and dividers may be installed anduninstalled as needed, as well as longitudinal dividers where necessary.Bulkheads and dividers may be installed using integrated slots andhooks, tabs, boss and channel, or hardware like bolts, nuts, screws,nails, clips, pins, and/or dowels, to name a few. Fuselage 204 may alsobe configurable to accept certain specific cargo containers, or areceptable that can, in turn, accept certain cargo containers.

Still referring to FIG. 2 , electric aircraft 200 may include aplurality of laterally extending elements attached to fuselage 204. Asused in this disclosure a “laterally extending element” is an elementthat projects essentially horizontally from fuselage, including anoutrigger, a spar, and/or a fixed wing that extends from fuselage. Wingsmay be structures which include airfoils configured to create a pressuredifferential resulting in lift. Wings may generally dispose on the leftand right sides of the aircraft symmetrically, at a point between noseand empennage. Wings may comprise a plurality of geometries in planformview, swept swing, tapered, variable wing, triangular, oblong,elliptical, square, among others. A wing's cross section geometry maycomprise an airfoil. An “airfoil” as used in this disclosure is a shapespecifically designed such that a fluid flowing above and below it exertdiffering levels of pressure against the top and bottom surface. Inembodiments, the bottom surface of an aircraft can be configured togenerate a greater pressure than does the top, resulting in lift.Laterally extending element may comprise differing and/or similarcross-sectional geometries over its cord length or the length from wingtip to where wing meets the aircraft's body. One or more wings may besymmetrical about the aircraft's longitudinal plane, which comprises thelongitudinal or roll axis reaching down the center of the aircraftthrough the nose and empennage, and the plane's yaw axis. Laterallyextending element may comprise controls surfaces configured to becommanded by a pilot or pilots to change a wing's geometry and thereforeits interaction with a fluid medium, like air. Control surfaces maycomprise flaps, ailerons, tabs, spoilers, and slats, among others. Thecontrol surfaces may dispose on the wings in a plurality of locationsand arrangements and in embodiments may be disposed at the leading andtrailing edges of the wings, and may be configured to deflect up, down,forward, aft, or a combination thereof. An aircraft, including adual-mode aircraft may comprise a combination of control surfaces toperform maneuvers while flying or on ground. In some embodiments,winglets may be provided at terminal ends of the wings which can provideimproved aerodynamic efficiency and stability in certain flightsituations. In some embodiments, the wings may be foldable to provide acompact aircraft profile, for example, for storage, parking and/or incertain flight modes.

Still referring to FIG. 2 , electric aircraft 200 may include aplurality of flight components 208. As used in this disclosure a “flightcomponent” is a component that promotes flight and guidance of anaircraft. Flight component 208 may include power sources, control linksto one or more elements, fuses, and/or mechanical couplings used todrive and/or control any other flight component. Flight component 208may include a motor that operates to move one or more flight controlcomponents, to drive one or more propulsors, or the like. A motor may bedriven by direct current (DC) electric power and may include, withoutlimitation, brushless DC electric motors, switched reluctance motors,induction motors, or any combination thereof. A motor may also includeelectronic speed controllers or other components for regulating motorspeed, rotation direction, and/or dynamic braking. Flight component 208may include an energy source. An energy source may include, for example,a generator, a photovoltaic device, a fuel cell such as a hydrogen fuelcell, direct methanol fuel cell, and/or solid oxide fuel cell, anelectric energy storage device (e.g. a capacitor, an inductor, and/or abattery). An energy source may also include a battery cell, or aplurality of battery cells connected in series into a module and eachmodule connected in series or in parallel with other modules.Configuration of an energy source containing connected modules may bedesigned to meet an energy or power requirement and may be designed tofit within a designated footprint in an electric aircraft.

Still referring to FIG. 2 , in an embodiment, flight component 208 maybe mechanically coupled to an aircraft. As used herein, a person ofordinary skill in the art would understand “mechanically coupled” tomean that at least a portion of a device, component, or circuit isconnected to at least a portion of the aircraft via a mechanicalcoupling. Said mechanical coupling can include, for example, rigidcoupling, such as beam coupling, bellows coupling, bushed pin coupling,constant velocity, split-muff coupling, diaphragm coupling, disccoupling, donut coupling, elastic coupling, flexible coupling, fluidcoupling, gear coupling, grid coupling, hirth joints, hydrodynamiccoupling, jaw coupling, magnetic coupling, Oldham coupling, sleevecoupling, tapered shaft lock, twin spring coupling, rag joint coupling,universal joints, or any combination thereof. In an embodiment,mechanical coupling may be used to connect the ends of adjacent partsand/or objects of an electric aircraft. Further, in an embodiment,mechanical coupling may be used to join two pieces of rotating electricaircraft components.

Still referring to FIG. 2 , in an embodiment, plurality of flightcomponents 208 of aircraft 200 may include at least a lift component 212and at least a pusher component 216. Flight component 208 may include apropulsor, a propeller, a motor, rotor, a rotating element, electricalenergy source, battery, and the like, among others. Each flightcomponent may be configured to generate lift and flight of electricaircraft. In some embodiments, flight component 208 may include one ormore lift components 212, one or more pusher components 216, one or morebattery packs including one or more batteries or cells, and one or moreelectric motors. Flight component 208 may include a propulsor. As usedin this disclosure a “propulsor component” or “propulsor” is a componentand/or device used to propel a craft by exerting force on a fluidmedium, which may include a gaseous medium such as air or a liquidmedium such as water. In an embodiment, when a propulsor twists andpulls air behind it, it may, at the same time, push an aircraft forwardwith an amount of force and/or thrust. More air pulled behind anaircraft results in greater thrust with which the aircraft is pushedforward. Propulsor component may include any device or component thatconsumes electrical power on demand to propel an electric aircraft in adirection or other vehicle while on ground or in-flight.

Still referring to FIG. 2 , in some embodiments, lift component 212 mayinclude a propulsor, a propeller, a blade, a motor, a rotor, a rotatingelement, an aileron, a rudder, arrangements thereof, combinationsthereof, and the like. Each lift component 212, when a plurality ispresent, of plurality of flight components 208 is configured to produce,in an embodiment, substantially upward and/or vertical thrust such thataircraft moves upward.

With continued reference to FIG. 2 , as used in this disclosure a “liftcomponent” is a component and/or device used to propel a craft upward byexerting downward force on a fluid medium, which may include a gaseousmedium such as air or a liquid medium such as water. Lift component 212may include any device or component that consumes electrical power ondemand to propel an electric aircraft in a direction or other vehiclewhile on ground or in-flight. For example, and without limitation, liftcomponent 212 may include a rotor, propeller, paddle wheel and the likethereof, wherein a rotor is a component that produces torque along thelongitudinal axis, and a propeller produces torque along the verticalaxis. In an embodiment, lift component 212 includes a plurality ofblades. As used in this disclosure a “blade” is a propeller thatconverts rotary motion from an engine or other power source into aswirling slipstream. In an embodiment, blade may convert rotary motionto push the propeller forwards or backwards. In an embodiment liftcomponent 212 may include a rotating power-driven hub, to which areattached several radial airfoil-section blades such that the wholeassembly rotates about a longitudinal axis. Blades may be configured atan angle of attack. In an embodiment, and without limitation, angle ofattack may include a fixed angle of attack. As used in this disclosure a“fixed angle of attack” is fixed angle between a chord line of a bladeand relative wind. As used in this disclosure a “fixed angle” is anangle that is secured and/or unmovable from the attachment point. In anembodiment, and without limitation, angle of attack may include avariable angle of attack. As used in this disclosure a “variable angleof attack” is a variable and/or moveable angle between a chord line of ablade and relative wind. As used in this disclosure a “variable angle”is an angle that is moveable from an attachment point. In an embodiment,angle of attack be configured to produce a fixed pitch angle. As used inthis disclosure a “fixed pitch angle” is a fixed angle between a cordline of a blade and the rotational velocity direction. In an embodimentfixed angle of attack may be manually variable to a few set positions toadjust one or more lifts of the aircraft prior to flight. In anembodiment, blades for an aircraft are designed to be fixed to their hubat an angle similar to the thread on a screw makes an angle to theshaft; this angle may be referred to as a pitch or pitch angle whichwill determine a speed of forward movement as the blade rotates.

In an embodiment, and still referring to FIG. 2 , lift component 212 maybe configured to produce a lift. As used in this disclosure a “lift” isa perpendicular force to the oncoming flow direction of fluidsurrounding the surface. For example, and without limitation relativeair speed may be horizontal to the aircraft, wherein lift force may be aforce exerted in a vertical direction, directing the aircraft upwards.In an embodiment, and without limitation, lift component 212 may producelift as a function of applying a torque to lift component. As used inthis disclosure a “torque” is a measure of force that causes an objectto rotate about an axis in a direction. For example, and withoutlimitation, torque may rotate an aileron and/or rudder to generate aforce that may adjust and/or affect altitude, airspeed velocity,groundspeed velocity, direction during flight, and/or thrust. Forexample, one or more flight components 208 such as a power source(s) mayapply a torque on lift component 212 to produce lift.

In an embodiment and still referring to FIG. 2 , a plurality of liftcomponents 212 of plurality of flight components 208 may be arranged ina quad copter orientation. As used in this disclosure a “quad copterorientation” is at least a lift component oriented in a geometric shapeand/or pattern, wherein each of the lift components is located along avertex of the geometric shape. For example, and without limitation, asquare quad copter orientation may have four lift propulsor componentsoriented in the geometric shape of a square, wherein each of the fourlift propulsor components are located along the four vertices of thesquare shape. As a further non-limiting example, a hexagonal quad copterorientation may have six lift components oriented in the geometric shapeof a hexagon, wherein each of the six lift components are located alongthe six vertices of the hexagon shape. In an embodiment, and withoutlimitation, quad copter orientation may include a first set of liftcomponents and a second set of lift components, wherein the first set oflift components and the second set of lift components may include twolift components each, wherein the first set of lift components and asecond set of lift components are distinct from one another. Forexample, and without limitation, the first set of lift components mayinclude two lift components that rotate in a clockwise direction,wherein the second set of lift propulsor components may include two liftcomponents that rotate in a counterclockwise direction. In anembodiment, and without limitation, the first set of lift components maybe oriented along a line oriented 45° from the longitudinal axis ofaircraft 200. In another embodiment, and without limitation, the secondset of lift components may be oriented along a line oriented 135° fromthe longitudinal axis, wherein the first set of lift components line andthe second set of lift components are perpendicular to each other.

Still referring to FIG. 2 , pusher component 216 and lift component 212(of flight component(s) 208) may include any such components and relateddevices as disclosed in U.S. Nonprovisional application Ser. No.16/427,298, filed on May 30, 2019, entitled “SELECTIVELY DEPLOYABLEHEATED PROPULSOR SYSTEM,” (Attorney Docket No. 1024-003USU1), U.S.Nonprovisional application Ser. No. 16/703,225, filed on Dec. 4, 2019,entitled “AN INTEGRATED ELECTRIC PROPULSION ASSEMBLY,” (Attorney DocketNo. 1024-009USU1), U.S. Nonprovisional application Ser. No. 16/910,255,filed on Jun. 24, 2020, entitled “AN INTEGRATED ELECTRIC PROPULSIONASSEMBLY,” (Attorney Docket No. 1024-009USC1), U.S. Nonprovisionalapplication Ser. No. 17/319,155, filed on May 13, 2021, entitled“AIRCRAFT HAVING REVERSE THRUST CAPABILITIES,” (Attorney Docket No.1024-028USU1), U.S. Nonprovisional application Ser. No. 16/929,206,filed on Jul. 15, 2020, entitled “A HOVER AND THRUST CONTROL ASSEMBLYFOR DUAL-MODE AIRCRAFT,” (Attorney Docket No. 1024-034USU1), U.S.Nonprovisional application Ser. No. 17/001,845, filed on Aug. 25, 2020,entitled “A HOVER AND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT,”(Attorney Docket No. 1024-034USC1), U.S. Nonprovisional application Ser.No. 17/186,079, filed on Feb. 26, 2021, entitled “METHODS AND SYSTEM FORESTIMATING PERCENTAGE TORQUE PRODUCED BY A PROPULSOR CONFIGURED FOR USEIN AN ELECTRIC AIRCRAFT,” (Attorney Docket No. 1024-079USU1), and U.S.Nonprovisional application Ser. No. 17/321,662, filed on May 17, 2021,entitled “AIRCRAFT FOR FIXED PITCH LIFT,” (Attorney Docket No.1024-103USU1), the entirety of each one of which is incorporated hereinby reference. Any aircrafts, including electric and eVTOL aircrafts, asdisclosed in any of these applications may efficaciously be utilizedwith any of the embodiments as disclosed herein, as needed or desired.Any flight controllers as disclosed in any of these applications mayefficaciously be utilized with any of the embodiments as disclosedherein, as needed or desired.

Still referring to FIG. 2 , pusher component 216 may include apropulsor, a propeller, a blade, a motor, a rotor, a rotating element,an aileron, a rudder, arrangements thereof, combinations thereof, andthe like. Each pusher component 216, when a plurality is present, of theplurality of flight components 208 is configured to produce, in anembodiment, substantially forward and/or horizontal thrust such that theaircraft moves forward.

Still referring to FIG. 2 , as used in this disclosure a “pushercomponent” is a component that pushes and/or thrusts an aircraft througha medium. As a non-limiting example, pusher component 216 may include apusher propeller, a paddle wheel, a pusher motor, a pusher propulsor,and the like. Additionally, or alternatively, pusher flight componentmay include a plurality of pusher flight components. Pusher component216 is configured to produce a forward thrust. As a non-limitingexample, forward thrust may include a force to force aircraft to in ahorizontal direction along the longitudinal axis. As a furthernon-limiting example, pusher component 216 may twist and/or rotate topull air behind it and, at the same time, push aircraft 200 forward withan equal amount of force. In an embodiment, and without limitation, themore air forced behind aircraft, the greater the thrust force with whichthe aircraft is pushed horizontally will be. In another embodiment, andwithout limitation, forward thrust may force aircraft 200 through themedium of relative air. Additionally or alternatively, plurality offlight components 208 may include one or more puller components. As usedin this disclosure a “puller component” is a component that pulls and/ortows an aircraft through a medium. As a non-limiting example, pullercomponent may include a flight component such as a puller propeller, apuller motor, a tractor propeller, a puller propulsor, and the like.Additionally, or alternatively, puller component may include a pluralityof puller flight components.

Still referring to FIG. 2 , as used in this disclosure a “power source”is a source that powers, drives and/or controls any flight componentand/or other aircraft component. For example, and without limitationpower source may include a motor that operates to move one or more liftcomponents 212 and/or one or more pusher components 216, to drive one ormore blades, or the like thereof. Motor(s) may be driven by directcurrent (DC) electric power and may include, without limitation,brushless DC electric motors, switched reluctance motors, inductionmotors, or any combination thereof. Motor(s) may also include electronicspeed controllers or other components for regulating motor speed,rotation direction, and/or dynamic braking. A “motor” as used in thisdisclosure is any machine that converts non-mechanical energy intomechanical energy. An “electric motor” as used in this disclosure is anymachine that converts electrical energy into mechanical energy.

Still referring to FIG. 2 , in an embodiment, aircraft 200 may include apilot control 220. As used in this disclosure, a “pilot control” is amechanism or means which allows a pilot to monitor and control operationof aircraft such as its flight components (for example, and withoutlimitation, pusher component, lift component and other components suchas propulsion components). For example, and without limitation, pilotcontrol 220 may include a collective, inceptor, foot bake, steeringand/or control wheel, control stick, pedals, throttle levers, and thelike. Pilot control 220 may be configured to translate a pilot's desiredtorque for each flight component of the plurality of flight components,such as and without limitation, pusher component 216 and lift component212. Pilot control 220 may be configured to control, via inputs and/orsignals such as from a pilot, the pitch, roll, and yaw of the aircraft.Pilot control may be available onboard aircraft or remotely located fromit, as needed or desired.

Still referring to FIG. 2 , as used in this disclosure a “collectivecontrol” or “collective” is a mechanical control of an aircraft thatallows a pilot to adjust and/or control the pitch angle of plurality offlight components 208. For example and without limitation, collectivecontrol may alter and/or adjust the pitch angle of all of the main rotorblades collectively. For example, and without limitation pilot control220 may include a yoke control. As used in this disclosure a “yokecontrol” is a mechanical control of an aircraft to control the pitchand/or roll. For example and without limitation, yoke control may alterand/or adjust the roll angle of aircraft 200 as a function ofcontrolling and/or maneuvering ailerons. In an embodiment, pilot control220 may include one or more foot-brakes, control sticks, pedals,throttle levels, and the like thereof. In another embodiment, andwithout limitation, pilot control 220 may be configured to control aprincipal axis of the aircraft. As used in this disclosure a “principalaxis” is an axis in a body representing one three dimensionalorientations. For example, and without limitation, principal axis ormore yaw, pitch, and/or roll axis. Principal axis may include a yawaxis. As used in this disclosure a “yaw axis” is an axis that isdirected towards the bottom of aircraft, perpendicular to the wings. Forexample, and without limitation, a positive yawing motion may includeadjusting and/or shifting nose of aircraft 200 to the right. Principalaxis may include a pitch axis. As used in this disclosure a “pitch axis”is an axis that is directed towards the right laterally extending wingof aircraft. For example, and without limitation, a positive pitchingmotion may include adjusting and/or shifting nose of aircraft 200upwards. Principal axis may include a roll axis. As used in thisdisclosure a “roll axis” is an axis that is directed longitudinallytowards nose of aircraft, parallel to fuselage. For example, and withoutlimitation, a positive rolling motion may include lifting the left andlowering the right wing concurrently. Pilot control 220 may beconfigured to modify a variable pitch angle. For example, and withoutlimitation, pilot control 220 may adjust one or more angles of attack ofa propulsor or propeller.

Still referring to FIG. 2 , aircraft 200 may include at least anaircraft sensor 228. Aircraft sensor 228 may include any sensor or noisemonitoring circuit described in this disclosure. Aircraft sensor 228, insome embodiments, may be communicatively connected or coupled to flightcontroller 124. Aircraft sensor 228 may be configured to sense acharacteristic of pilot control 220. Sensor may be a device, module,and/or subsystem, utilizing any hardware, software, and/or anycombination thereof to sense a characteristic and/or changes thereof, inan instant environment, for instance without limitation a pilot control220, which the sensor is proximal to or otherwise in a sensedcommunication with, and transmit information associated with thecharacteristic, for instance without limitation digitized data. Sensor228 may be mechanically and/or communicatively coupled to aircraft 200,including, for instance, to at least a pilot control 220. Aircraftsensor 228 may be configured to sense a characteristic associated withat least a pilot control 220. An environmental sensor may includewithout limitation one or more sensors used to detect ambienttemperature, barometric pressure, and/or air velocity. Aircraft sensor228 may include without limitation gyroscopes, accelerometers, inertialmeasurement unit (IMU), and/or magnetic sensors, one or more humiditysensors, one or more oxygen sensors, or the like. Additionally oralternatively, sensor 228 may include at least a geospatial sensor.Aircraft sensor 228 may be located inside aircraft, and/or be includedin and/or attached to at least a portion of aircraft. Sensor may includeone or more proximity sensors, displacement sensors, vibration sensors,and the like thereof. Sensor may be used to monitor the status ofaircraft 200 for both critical and non-critical functions. Sensor may beincorporated into vehicle or aircraft or be remote.

Still referring to FIG. 2 , in some embodiments, aircraft sensor 228 maybe configured to sense a characteristic associated with any pilotcontrol described in this disclosure. Non-limiting examples of aircraftsensor 228 may include an inertial measurement unit (IMU), anaccelerometer, a gyroscope, a proximity sensor, a pressure sensor, alight sensor, a pitot tube, an air speed sensor, a position sensor, aspeed sensor, a switch, a thermometer, a strain gauge, an acousticsensor, and an electrical sensor. In some cases, aircraft sensor 228 maysense a characteristic as an analog measurement, for instance, yieldinga continuously variable electrical potential indicative of the sensedcharacteristic. In these cases, aircraft sensor 228 may additionallycomprise an analog to digital converter (ADC) as well as anyadditionally circuitry, such as without limitation a Wheatstone bridge,an amplifier, a filter, and the like. For instance, in some cases,aircraft sensor 228 may comprise a strain gage configured to determineloading of one or more aircraft components, for instance landing gear.Strain gage may be included within a circuit comprising a Wheatstonebridge, an amplified, and a bandpass filter to provide an analog strainmeasurement signal having a high signal to noise ratio, whichcharacterizes strain on a landing gear member. An ADC may then digitizeanalog signal produces a digital signal that can then be transmittedother systems within aircraft 200, for instance without limitation acomputing system, a pilot display, and a memory component. Alternativelyor additionally, aircraft sensor 228 may sense a characteristic of apilot control 220 digitally. For instance in some embodiments, aircraftsensor 228 may sense a characteristic through a digital means ordigitize a sensed signal natively. In some cases, for example, aircraftsensor 228 may include a rotational encoder and be configured to sense arotational position of a pilot control; in this case, the rotationalencoder digitally may sense rotational “clicks” by any known method,such as without limitation magnetically, optically, and the like.Aircraft sensor 228 may include any of the sensors as disclosed in thepresent disclosure. Aircraft sensor 228 may include a plurality ofsensors. Any of these sensors may be located at any suitable position inor on aircraft 200.

With continued reference to FIG. 2 , in some embodiments, electricaircraft 200 includes, or may be coupled to or communicatively connectedto, flight controller 124 which is described further with reference toFIG. 3 . As used in this disclosure a “flight controller” is a computingdevice of a plurality of computing devices dedicated to data storage,security, distribution of traffic for load balancing, and flightinstruction. In embodiments, flight controller may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith. Flight controller 124, in an embodiment, is located withinfuselage 204 of aircraft. In accordance with some embodiments, flightcontroller is configured to operate a vertical lift flight (upwards ordownwards, that is, takeoff or landing), a fixed wing flight (forward orbackwards), a transition between a vertical lift flight and a fixed wingflight, and a combination of a vertical lift flight and a fixed wingflight.

Still referring to FIG. 2 , in an embodiment, and without limitation,flight controller 124 may be configured to operate a fixed-wing flightcapability. A “fixed-wing flight capability” can be a method of flightwherein the plurality of laterally extending elements generate lift. Forexample, and without limitation, fixed-wing flight capability maygenerate lift as a function of an airspeed of aircraft 200 and one ormore airfoil shapes of the laterally extending elements. As a furthernon-limiting example, flight controller 124 may operate the fixed-wingflight capability as a function of reducing applied torque on lift(propulsor) component 212. In an embodiment, and without limitation, anamount of lift generation may be related to an amount of forward thrustgenerated to increase airspeed velocity, wherein the amount of liftgeneration may be directly proportional to the amount of forward thrustproduced. Additionally or alternatively, flight controller may includean inertia compensator. As used in this disclosure an “inertiacompensator” is one or more computing devices, electrical components,logic circuits, processors, and the like there of that are configured tocompensate for inertia in one or more lift (propulsor) componentspresent in aircraft 100. Inertia compensator may alternatively oradditionally include any computing device used as an inertia compensatoras described in U.S. Nonprovisional application Ser. No. 17/106,557,filed on Nov. 30, 2020, and entitled “SYSTEM AND METHOD FOR FLIGHTCONTROL IN ELECTRIC AIRCRAFT,” the entirety of which is incorporatedherein by reference. Flight controller 124 may efficaciously include anyflight controllers as disclosed in U.S. Nonprovisional application Ser.No. 17/106,557, filed on Nov. 30, 2020, and entitled “SYSTEM AND METHODFOR FLIGHT CONTROL IN ELECTRIC AIRCRAFT.”

In an embodiment, and still referring to FIG. 2 , flight controller 124may be configured to perform a reverse thrust command. As used in thisdisclosure a “reverse thrust command” is a command to perform a thrustthat forces a medium towards the relative air opposing aircraft 100.Reverse thrust command may alternatively or additionally include anyreverse thrust command as described in U.S. Nonprovisional applicationSer. No. 17/319,155, filed on May 13, 2021, and entitled “AIRCRAFTHAVING REVERSE THRUST CAPABILITIES,” the entirety of which isincorporated herein by reference. In another embodiment, flightcontroller may be configured to perform a regenerative drag operation.As used in this disclosure a “regenerative drag operation” is anoperating condition of an aircraft, wherein the aircraft has a negativethrust and/or is reducing in airspeed velocity. For example, and withoutlimitation, regenerative drag operation may include a positive propellerspeed and a negative propeller thrust. Regenerative drag operation mayalternatively or additionally include any regenerative drag operation asdescribed in U.S. Nonprovisional application Ser. No. 17/319,155. Flightcontroller 124 may efficaciously include any flight controllers asdisclosed in U.S. Nonprovisional application Ser. No. 17/319,155, filedon May 13, 2021, and entitled “AIRCRAFT HAVING REVERSE THRUSTCAPABILITIES,” (Attorney Docket No. 1024-028USU1).

In an embodiment, and still referring to FIG. 2 , flight controller 124may be configured to perform a corrective action as a function of afailure event. As used in this disclosure a “corrective action” is anaction conducted by the plurality of flight components to correct and/oralter a movement of an aircraft. For example, and without limitation, acorrective action may include an action to reduce a yaw torque generatedby a failure event. Additionally or alternatively, corrective action mayinclude any corrective action as described in U.S. Nonprovisionalapplication Ser. No. 17/222,539, filed on Apr. 5, 2021, and entitled“AIRCRAFT FOR SELF-NEUTRALIZING FLIGHT,” the entirety of which isincorporated herein by reference. As used in this disclosure a “failureevent” is a failure of a lift component of the plurality of liftcomponents. For example, and without limitation, a failure event maydenote a rotation degradation of a rotor, a reduced torque of a rotor,and the like thereof. Additionally or alternatively, failure event mayinclude any failure event as described in U.S. Nonprovisionalapplication Ser. No. 17/113,647, filed on Dec. 7, 2020, and entitled“IN-FLIGHT STABILIZATION OF AN AIRCRAFT,” the entirety of which isincorporated herein by reference. Flight controller 124 mayefficaciously include any flight controllers as disclosed in U.S.Nonprovisional application Ser. Nos. 17/222,539 and 17/113,647.

With continued reference to FIG. 2 , flight controller 124 may includeone or more computing devices. Computing device may include anycomputing device as described in this disclosure. Flight controller 124may be onboard aircraft 200 and/or flight controller 124 may be remotefrom aircraft 200, as long as, in some embodiments, flight controller124 is communicatively connected to aircraft 200. As used in thisdisclosure, “remote” is a spatial separation between two or moreelements, systems, components or devices. Stated differently, twoelements may be remote from one another if they are physically spacedapart. In an embodiment, flight controller 124 may include aproportional-integral-derivative (PID) controller.

Now referring to FIG. 3 , an exemplary embodiment 300 of a flightcontroller 304 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 304 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 304may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 304 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include a signal transformation component 308. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 308 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component308 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 308 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 308 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 308 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 3 , signal transformation component 308 may beconfigured to optimize an intermediate representation 312. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 308 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 308 may optimizeintermediate representation 312 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 308 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 308 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 304. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

Still referring to FIG. 3 , in an embodiment, and without limitation,signal transformation component 308 may include transform one or moreinputs and outputs as a function of an error correction code. An errorcorrection code, also known as error correcting code (ECC), is anencoding of a message or lot of data using redundant information,permitting recovery of corrupted data. An ECC may include a block code,in which information is encoded on fixed-size packets and/or blocks ofdata elements such as symbols of predetermined size, bits, or the like.Reed-Solomon coding, in which message symbols within a symbol set havingq symbols are encoded as coefficients of a polynomial of degree lessthan or equal to a natural number k, over a finite field F with qelements; strings so encoded have a minimum hamming distance of k+1, andpermit correction of (q−k−1)/2 erroneous symbols. Block code mayalternatively or additionally be implemented using Golay coding, alsoknown as binary Golay coding, Bose-Chaudhuri, Hocquenghuem (BCH) coding,multidimensional parity-check coding, and/or Hamming codes. An ECC mayalternatively or additionally be based on a convolutional code.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include a reconfigurable hardware platform 316. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 316 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 3 , reconfigurable hardware platform 316 mayinclude a logic component 320. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 320 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 320 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 320 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 320 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 320 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 312. Logiccomponent 320 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 304. Logiccomponent 320 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 320 may beconfigured to execute the instruction on intermediate representation 312and/or output language. For example, and without limitation, logiccomponent 320 may be configured to execute an addition operation onintermediate representation 312 and/or output language.

In an embodiment, and without limitation, logic component 320 may beconfigured to calculate a flight element 324. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 324 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 324 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 324 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 3 , flight controller 304 may include a chipsetcomponent 328. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 328 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 320 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 328 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 320 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 328 maymanage data flow between logic component 320, memory cache, and a flightcomponent 208. As used in this disclosure (and with particular referenceto FIG. 3 ) a “flight component” is a portion of an aircraft that can bemoved or adjusted to affect one or more flight elements. For example,flight component 208 may include a component used to affect theaircrafts' roll and pitch which may comprise one or more ailerons. As afurther example, flight component 208 may include a rudder to controlyaw of an aircraft. In an embodiment, chipset component 328 may beconfigured to communicate with a plurality of flight components as afunction of flight element 324. For example, and without limitation,chipset component 328 may transmit to an aircraft rotor to reduce torqueof a first lift propulsor and increase the forward thrust produced by apusher component to perform a flight maneuver.

In an embodiment, and still referring to FIG. 3 , flight controller 304may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 304 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 324. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 304 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 304 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 3 , flight controller 304may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 324 and a pilot signal336 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 336may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 336 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 336may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 336 may include an explicitsignal directing flight controller 304 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 336 may include an implicit signal, wherein flight controller 304detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 336 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 336 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 336 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 336 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal336 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 3 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller 304 and/or a remote device may or may not use inthe generation of autonomous function. As used in this disclosure“remote device” is an external device to flight controller 304.Additionally or alternatively, autonomous machine-learning model mayinclude one or more autonomous machine-learning processes that afield-programmable gate array (FPGA) may or may not use in thegeneration of autonomous function. Autonomous machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naïve bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

In an embodiment, and still referring to FIG. 3 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 304 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 3 , flight controller 304 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 304. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 304 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 304 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 3 , flight controller 304 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller304 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 304 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 304 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Massachusetts, USA. In an embodiment, and withoutlimitation, control algorithm may be configured to generate anauto-code, wherein an “auto-code,” is used herein, is a code and/oralgorithm that is generated as a function of the one or more modelsand/or software's. In another embodiment, control algorithm may beconfigured to produce a segmented control algorithm. As used in thisdisclosure a “segmented control algorithm” is control algorithm that hasbeen separated and/or parsed into discrete sections. For example, andwithout limitation, segmented control algorithm may parse controlalgorithm into two or more segments, wherein each segment of controlalgorithm may be performed by one or more flight controllers operatingon distinct flight components.

In an embodiment, and still referring to FIG. 3 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 208. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 3 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 304. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 312 and/or output language from logiccomponent 320, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 3 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 3 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 3 , flight controller 304 may also beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofaircraft and/or computing device. Flight controller 304 may include adistributer flight controller. As used in this disclosure a “distributerflight controller” is a component that adjusts and/or controls aplurality of flight components as a function of a plurality of flightcontrollers. For example, distributer flight controller may include aflight controller that communicates with a plurality of additionalflight controllers and/or clusters of flight controllers. In anembodiment, distributed flight control may include one or more neuralnetworks. For example, neural network also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 3 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 3 , flight controller may include asub-controller 340. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 304 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 340may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 340 may include any component of any flightcontroller as described above. Sub-controller 340 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 340may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 340 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 3 , flight controller may include aco-controller 344. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller 304 ascomponents and/or nodes of a distributer flight controller as describedabove. For example, and without limitation, co-controller 344 mayinclude one or more controllers and/or components that are similar toflight controller 304. As a further non-limiting example, co-controller344 may include any controller and/or component that joins flightcontroller 304 to distributer flight controller. As a furthernon-limiting example, co-controller 344 may include one or moreprocessors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data to and/or from flightcontroller 304 to distributed flight control system. Co-controller 344may include any component of any flight controller as described above.Co-controller 344 may be implemented in any manner suitable forimplementation of a flight controller as described above.

In an embodiment, and with continued reference to FIG. 3 , flightcontroller 304 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 304 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 4 , an embodiment of sensor suite 400 ispresented. The herein disclosed system and method may comprise aplurality of sensors in the form of individual sensors or a sensor suiteworking in tandem or individually. A sensor suite may include aplurality of independent sensors, as described herein, where any numberof the described sensors may be used to detect any number of physical orelectrical quantities associated with an aircraft power system or anelectrical energy storage system. Independent sensors may includeseparate sensors measuring physical or electrical quantities that may bepowered by and/or in communication with circuits independently, whereeach may signal sensor output to a control circuit such as a usergraphical interface. In a non-limiting example, there may be fourindependent sensors housed in and/or on battery pack 424 measuringtemperature, electrical characteristic such as voltage, amperage,resistance, or impedance, or any other parameters and/or quantities asdescribed in this disclosure. In an embodiment, use of a plurality ofindependent sensors may result in redundancy configured to employ morethan one sensor that measures the same phenomenon, those sensors beingof the same type, a combination of, or another type of sensor notdisclosed, so that in the event one sensor fails, the ability of batterymanagement system 400 and/or user to detect phenomenon is maintained andin a non-limiting example, a user alter aircraft usage pursuant tosensor readings.

Still referring to FIG. 4 , sensor suite 400 may include a moisturesensor 404. “Moisture”, as used in this disclosure, is the presence ofwater, this may include vaporized water in air, condensation on thesurfaces of objects, or concentrations of liquid water. Moisture mayinclude humidity. “Humidity”, as used in this disclosure, is theproperty of a gaseous medium (almost always air) to hold water in theform of vapor. An amount of water vapor contained within a parcel of aircan vary significantly. Water vapor is generally invisible to the humaneye and may be damaging to electrical components. There are threeprimary measurements of humidity, absolute, relative, specific humidity.“Absolute humidity,” for the purposes of this disclosure, describes thewater content of air and is expressed in either grams per cubic metersor grams per kilogram. “Relative humidity”, for the purposes of thisdisclosure, is expressed as a percentage, indicating a present stat ofabsolute humidity relative to a maximum humidity given the sametemperature. “Specific humidity”, for the purposes of this disclosure,is the ratio of water vapor mass to total moist air parcel mass, whereparcel is a given portion of a gaseous medium. Moisture sensor 404 maybe psychrometer. Moisture sensor 404 may be a hygrometer. Moisturesensor 404 may be configured to act as or include a humidistat. A“humidistat”, for the purposes of this disclosure, is ahumidity-triggered switch, often used to control another electronicdevice. Moisture sensor 404 may use capacitance to measure relativehumidity and include in itself, or as an external component, include adevice to convert relative humidity measurements to absolute humiditymeasurements. “Capacitance”, for the purposes of this disclosure, is theability of a system to store an electric charge, in this case the systemis a parcel of air which may be near, adjacent to, or above a batterycell.

With continued reference to FIG. 4 , sensor suite 400 may includeelectrical sensors 408. Electrical sensors 408 may be configured tomeasure voltage across a component, electrical current through acomponent, and resistance of a component. Electrical sensors 408 mayinclude separate sensors to measure each of the previously disclosedelectrical characteristics such as voltmeter, ammeter, and ohmmeter,respectively.

Alternatively or additionally, and with continued reference to FIG. 4 ,sensor suite 400 include a sensor or plurality thereof that may detectvoltage and direct the charging of individual battery cells according tocharge level; detection may be performed using any suitable component,set of components, and/or mechanism for direct or indirect measurementand/or detection of voltage levels, including without limitationcomparators, analog to digital converters, any form of voltmeter, or thelike. Sensor suite 400 and/or a control circuit incorporated thereinand/or communicatively connected thereto may be configured to adjustcharge to one or more battery cells as a function of a charge leveland/or a detected parameter. For instance, and without limitation,sensor suite 400 may be configured to determine that a charge level of abattery cell is high based on a detected voltage level of that batterycell or portion of the battery pack. Sensor suite 400 may alternativelyor additionally detect a charge reduction event, defined for purposes ofthis disclosure as any temporary or permanent state of a battery cellrequiring reduction or cessation of charging; a charge reduction eventmay include a cell being fully charged and/or a cell undergoing aphysical and/or electrical process that makes continued charging at acurrent voltage and/or current level inadvisable due to a risk that thecell will be damaged, will overheat, or the like. Detection of a chargereduction event may include detection of a temperature, of the cellabove a threshold level, detection of a voltage and/or resistance levelabove or below a threshold, or the like. Sensor suite 400 may includedigital sensors, analog sensors, or a combination thereof. Sensor suite400 may include digital-to-analog converters (DAC), analog-to-digitalconverters (ADC, A/D, A-to-D), a combination thereof, or other signalconditioning components used in transmission of a first plurality ofbattery pack data 428 to a destination over wireless or wiredconnection.

With continued reference to FIG. 4 , sensor suite 400 may includethermocouples, thermistors, thermometers, passive infrared sensors,resistance temperature sensors (RTD's), semiconductor based integratedcircuits (IC), a combination thereof or another undisclosed sensor type,alone or in combination. Temperature, for the purposes of thisdisclosure, and as would be appreciated by someone of ordinary skill inthe art, is a measure of the heat energy of a system. Temperature, asmeasured by any number or combinations of sensors present within sensorsuite 400, may be measured in Fahrenheit (° F.), Celsius (° C.), Kelvin(° K), or another scale alone or in combination. The temperaturemeasured by sensors may comprise electrical signals which aretransmitted to their appropriate destination wireless or through a wiredconnection.

With continued reference to FIG. 4 , sensor suite 400 may include asensor configured to detect gas that may be emitted during or after acell failure. “Cell failure”, for the purposes of this disclosure,refers to a malfunction of a battery cell, which may be anelectrochemical cell, that renders the cell inoperable for its designedfunction, namely providing electrical energy to at least a portion of anelectric aircraft. Byproducts of cell failure 412 may include gaseousdischarge including oxygen, hydrogen, carbon dioxide, methane, carbonmonoxide, a combination thereof, or another undisclosed gas, alone or incombination. Further the sensor configured to detect vent gas fromelectrochemical cells may comprise a gas detector. For the purposes ofthis disclosure, a “gas detector” is a device used to detect a gas ispresent in an area. Gas detectors, and more specifically, the gas sensorthat may be used in sensor suite 400, may be configured to detectcombustible, flammable, toxic, oxygen depleted, a combination thereof,or another type of gas alone or in combination. The gas sensor that maybe present in sensor suite 400 may include a combustible gas,photoionization detectors, electrochemical gas sensors, ultrasonicsensors, metal-oxide-semiconductor (MOS) sensors, infrared imagingsensors, a combination thereof, or another undisclosed type of gassensor alone or in combination. Sensor suite 400 may include sensorsthat are configured to detect non-gaseous byproducts of cell failure 412including, in non-limiting examples, liquid chemical leaks includingaqueous alkaline solution, ionomer, molten phosphoric acid, liquidelectrolytes with redox shuttle and ionomer, and salt water, amongothers. Sensor suite 400 may include sensors that are configured todetect non-gaseous byproducts of cell failure 412 including, innon-limiting examples, electrical anomalies as detected by any of theprevious disclosed sensors or components.

With continued reference to FIG. 4 , sensor suite 400 may be configuredto detect events where voltage nears an upper voltage threshold or lowervoltage threshold. An upper voltage threshold may be stored in a datastorage system for comparison with an instant measurement taken by anycombination of sensors present within sensor suite 400. An upper voltagethreshold may be calculated and calibrated based on factors relating tobattery cell health, maintenance history, location within battery pack,designed application, and type, among others. Sensor suite 400 maymeasure voltage at an instant, over a period of time, or periodically.Sensor suite 400 may be configured to operate at any of these detectionmodes, switch between modes, or simultaneous measure in more than onemode. A lower voltage threshold may indicate power loss to or from anindividual battery cell or portion of the battery pack. Events wherevoltage exceeds the upper and lower voltage threshold may indicatebattery cell failure or electrical anomalies that could lead topotentially dangerous situations for aircraft and personnel that may bepresent in or near its operation.

Referring now to FIG. 5 , indicator database 500 is presented. Databasemay be implemented, without limitation, as a relational database, akey-value retrieval database such as a NOSQL database, or any otherformat or structure for use as a database that a person skilled in theart would recognize as suitable upon review of the entirety of thisdisclosure. Database may alternatively or additionally be implementedusing a distributed data storage protocol and/or data structure, such asa distributed hash table or the like. Database may include a pluralityof data entries and/or records as described above. Data entries in adatabase may be flagged with or linked to one or more additionalelements of information, which may be reflected in data entry cellsand/or in linked tables such as tables related by one or more indices ina relational database. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in which dataentries in a database may store, retrieve, organize, and/or reflect dataand/or records as used herein, as well as categories and/or populationsof data consistently with this disclosure.

Still referring to FIG. 5 , indicator database 500 may include aircraftdata 116. Aircraft data 116 may include, but is not limited to,altitude, aircraft speed, rotor torques, temperatures, flight modes,battery health, battery state of charge, and the like. Aircraft data 116may be as described above.

Still referring to FIG. 5 , indicator database 500 may include flightconfidence parameter data 504. Flight confidence parameter data 504 mayinclude information of flight confidence parameters. Flight confidenceparameter data 504 may include, but is not limited to, temperatures,torques, battery health, battery state of charge, inverter temperature,power dissipation levels, rotor and/or motor temperatures, altitudes,aircraft speeds, flight modes, and the like. Flight confidence parameterdata 504 may be updated by user input, an external computing device,and/or from iterations of flight of an electric aircraft. As anon-limiting example, an electric aircraft may initially have a baseflight confidence parameter of inverter temperatures of 40 degreesCelsius. An electric aircraft may be shown to optimally fly withinverter temperatures of 34 degrees Celsius, in which case flightconfidence parameter data 504 may update with the inverter temperatureof 34 degrees Celsius. Flight confidence parameters may be determined byan optimization model and/or machine learning model as described abovewith reference to FIG. 1 .

Still referring to FIG. 5 , indicator database 500 may include flightparameter threshold data 508. Flight parameter threshold data 508 mayinclude information of values of flight parameters that are sought to beminimized or maximized. Flight parameter threshold data 508 may include,but is not limited to, temperature thresholds, such as temperatures ofbatteries, inverters, rotors, motors, and the like. Flight parameterthreshold data 508 may include, but is not limited to, altitudethresholds, speed thresholds, torque thresholds, flight mode durationthresholds, and the like. Flight parameter threshold data 508 may bereceived from user input, external computing devices, and/or previousflights of an electric aircraft. As a non-limiting, an electric aircraftmay initially have a torque threshold of 450 N*m. An electric aircraftmay be shown to sustain torques of up to 500 N*m without damage to therotor and/or motor. Continuing this example, flight parameter thresholddata 508 may be updated to include the new torque threshold of 500 N*m.Flight parameter threshold data 508 may be determined by a machinelearning model.

Still referring to FIG. 5 , indicator database 500 may includehistorical performance data 512. Historical performance data 512 mayinclude information of flight performance of one or more electricaircraft. Historical performance data 512 may include flight ranges ofcompleted flights. As a non-limiting example, historical performancedata 512 may show data of an electric aircraft completing a trip of 250miles on a single charge. Historical performance data 512 may includeperformance data of one or more parts of an electric aircraft such as,but not limited to, battery packs, battery cells, rotors, motors,inverters, propulsors, and the like. Historical performance data 512 maybe used by a machine learning model to predict flight ranges of electricaircraft and/or most limiting parameters.

Referring now to FIG. 6 , method 600 of determining a most limitingparameter of an electric aircraft is presented. At step 605, method 600includes measuring a parameter of an electric aircraft. A parameter ofan electric aircraft may be measured through a sensing device. In someembodiments, a measurement of a parameter of an electric aircraft mayinclude, but is not limited to, a measurement of temperature, altitude,speed, torque output, and the like. A sensing device may measure aparameter of an electric aircraft and generate aircraft data from theparameter of the electric aircraft. This step may be implemented withoutlimitation as described above in FIGS. 1-5 .

Still referring to FIG. 6 , at step 610, method 600 includescommunicating aircraft data to at least a processor. Communicatingaircraft data may include wireless or wired communication between atleast a processor and a sensing device. This step may be implementedwithout limitation as described above in FIGS. 1-5 .

Still referring to FIG. 6 , at step 615, method 600 includes determininga most limiting parameter. A most limiting parameter may be determinedby at least a processor. In some embodiments, a most limiting parametermay include a flight parameter that may negatively impact a flight rangeof an electric aircraft. As a non-limiting example, a most limitingparameter may include, but is not limited to, rotor temperature, motortemperature, inverter temperature, torque output, altitude, heatingsystems, lighting systems, and the like. In some embodiments, a mostlimiting parameter may be related to at least a battery of an electricaircraft. A most limiting parameter may include, but is not limited to,battery pack temperature, battery pack charge, battery pack health, andthe like. In some embodiments, determining a most limiting parameter mayinclude comparing aircraft data to a flight confidence parameter. Insome embodiments, at least a processor may compare aircraft data to aflight confidence parameter. A comparison may include at least aprocessor generating an objective function. At least a processor may beconfigured to compare aircraft data to an optimization criterion. Insome embodiments, at least a processor may be configured to compareaircraft data to a flight parameter threshold. This step may beimplemented without limitation as described above in FIGS. 1-5 .

Still referring to FIG. 6 , at step 620, method 600 includescommunicating a most limiting parameter to a pilot indicator. A pilotindicator may include a visual and/or audio communication device suchas, but not limited to, screens, speakers, lights, and the like. Atleast a processor may communicate a most limiting parameter to a pilotindicator which may show a list of most limiting parameters, historicaltrends, and/or other aircraft data. In some embodiments, an operationalthreshold of one or more parts of an electric aircraft may be reached.At least a processor may alert a pilot through a pilot indicator of oneor more parts exceeding operational threshold limits of an electricaircraft. This step may be implemented without limitation as describedabove in FIGS. 1-5 .

Referring now to FIG. 7 , an exemplary embodiment of a machine-learningmodule 700 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process”, as used in thisdisclosure, is a process that automatedly uses training data 704 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 708 given data provided as inputs 712;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 7 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 704 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 704 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 704 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 704 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 704 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 704 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data704 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 7 ,training data 704 may include one or more elements that are notcategorized; that is, training data 704 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 704 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 704 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 704 used by machine-learning module 700 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample inputs may include aircraft data and outputs may include mostlimiting parameters.

Further referring to FIG. 7 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 716. Training data classifier 716 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 700 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 704. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 716 may classify elements of training data to flightparameters, such as battery pack temperatures, torque outputs, batterystate of charge, battery state of health, and the like.

Still referring to FIG. 7 , machine-learning module 700 may beconfigured to perform a lazy-learning process 720 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 704. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 704 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 7 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 724. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 724 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 724 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 704set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 7 , machine-learning algorithms may include atleast a supervised machine-learning process 728. At least a supervisedmachine-learning process 728, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude aircraft data as described above as inputs, most limitingparameters as outputs, and a scoring function representing a desiredform of relationship to be detected between inputs and outputs; scoringfunction may, for instance, seek to maximize the probability that agiven input and/or combination of elements inputs is associated with agiven output to minimize the probability that a given input is notassociated with a given output. Scoring function may be expressed as arisk function representing an “expected loss” of an algorithm relatinginputs to outputs, where loss is computed as an error functionrepresenting a degree to which a prediction generated by the relation isincorrect when compared to a given input-output pair provided intraining data 704. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various possiblevariations of at least a supervised machine-learning process 728 thatmay be used to determine relation between inputs and outputs. Supervisedmachine-learning processes may include classification algorithms asdefined above.

Further referring to FIG. 7 , machine learning processes may include atleast an unsupervised machine-learning processes 732. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 7 , machine-learning module 700 may be designedand configured to create a machine-learning model 724 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 7 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 800 includes a processor 804 and a memory808 that communicate with each other, and with other components, via abus 812. Bus 812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Still referring to FIG. 8 , processor 804 may include any suitableprocessor, such as without limitation a processor incorporating logicalcircuitry for performing arithmetic and logical operations, such as anarithmetic and logic unit (ALU), which may be regulated with a statemachine and directed by operational inputs from memory and/or sensors;processor 804 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Processor 804 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC).

Still referring to FIG. 8 , memory 808 may include various components(e.g., machine-readable media) including, but not limited to, arandom-access memory component, a read only component, and anycombinations thereof. In one example, a basic input/output system 816(BIOS), including basic routines that help to transfer informationbetween elements within computer system 800, such as during start-up,may be stored in memory 808. Memory 808 may also include (e.g., storedon one or more machine-readable media) instructions (e.g., software) 820embodying any one or more of the aspects and/or methodologies of thepresent disclosure. In another example, memory 808 may further includeany number of program modules including, but not limited to, anoperating system, one or more application programs, other programmodules, program data, and any combinations thereof.

Still referring to FIG. 8 , computer system 800 may also include astorage device 824. Examples of a storage device (e.g., storage device824) include, but are not limited to, a hard disk drive, a magnetic diskdrive, an optical disc drive in combination with an optical medium, asolid-state memory device, and any combinations thereof. Storage device824 may be connected to bus 812 by an appropriate interface (not shown).Example interfaces include, but are not limited to, SCSI, advancedtechnology attachment (ATA), serial ATA, universal serial bus (USB),IEEE 1394 (FIREWIRE), and any combinations thereof. In one example,storage device 824 (or one or more components thereof) may be removablyinterfaced with computer system 800 (e.g., via an external portconnector (not shown)). Particularly, storage device 824 and anassociated machine-readable medium 828 may provide nonvolatile and/orvolatile storage of machine-readable instructions, data structures,program modules, and/or other data for computer system 800. In oneexample, software 820 may reside, completely or partially, withinmachine-readable medium 828. In another example, software 820 mayreside, completely or partially, within processor 804.

Still referring to FIG. 8 , computer system 800 may also include aninput device 832. In one example, a user of computer system 800 mayenter commands and/or other information into computer system 800 viainput device 832. Examples of an input device 832 include, but are notlimited to, an alpha-numeric input device (e.g., a keyboard), a pointingdevice, a joystick, a gamepad, an audio input device (e.g., amicrophone, a voice response system, etc.), a cursor control device(e.g., a mouse), a touchpad, an optical scanner, a video capture device(e.g., a still camera, a video camera), a touchscreen, and anycombinations thereof. Input device 832 may be interfaced to bus 812 viaany of a variety of interfaces (not shown) including, but not limitedto, a serial interface, a parallel interface, a game port, a USBinterface, a FIREWIRE interface, a direct interface to bus 812, and anycombinations thereof. Input device 832 may include a touch screeninterface that may be a part of or separate from display 836, discussedfurther below. Input device 832 may be utilized as a user selectiondevice for selecting one or more graphical representations in agraphical interface as described above.

Still referring to FIG. 8 , a user may also input commands and/or otherinformation to computer system 800 via storage device 824 (e.g., aremovable disk drive, a flash drive, etc.) and/or network interfacedevice 840. A network interface device, such as network interface device840, may be utilized for connecting computer system 800 to one or moreof a variety of networks, such as network 844, and one or more remotedevices 848 connected thereto. Examples of a network interface deviceinclude, but are not limited to, a network interface card (e.g., amobile network interface card, a LAN card), a modem, and any combinationthereof. Examples of a network include, but are not limited to, a widearea network (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 844, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 820, etc.) may be communicated to and/or fromcomputer system 800 via network interface device 840.

Still referring to FIG. 8 , computer system 800 may further include avideo display adapter 852 for communicating a displayable image to adisplay device, such as display device 836. Examples of a display deviceinclude, but are not limited to, a liquid crystal display (LCD), acathode ray tube (CRT), a plasma display, a light emitting diode (LED)display, and any combinations thereof. Display adapter 852 and displaydevice 836 may be utilized in combination with processor 804 to providegraphical representations of aspects of the present disclosure. Inaddition to a display device, computer system 800 may include one ormore other peripheral output devices including, but not limited to, anaudio speaker, a printer, and any combinations thereof. Such peripheraloutput devices may be connected to bus 812 via a peripheral interface856. Examples of a peripheral interface include, but are not limited to,a serial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,apparatuses, and software according to the present disclosure.Accordingly, this description is meant to be taken only by way ofexample, and not to otherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1. An apparatus for determining a most limiting parameter in an electricaircraft, comprising: at least a processor; and a memory communicativelyconnected to the at least a processor, the memory containinginstructions configuring the at least a processor to: receive aircraftdata from a sensing device, wherein the sensing device is configured tomeasure a parameter of the electric aircraft and generate aircraft data;compare, by the at least a processor, the aircraft data to at least aflight confidence parameter using an optimization criterion, wherein theoptimization criterion is further configured to assign weights to atleast an attribute of a flight, and wherein the weights are furtherconfigured to indicate a relative importance of the at least anattribute of the flight; determine a most limiting parameter of theelectric aircraft as a function of the aircraft data, wherein the mostlimiting parameter is related to at least a component of the electricaircraft with a greatest effect on a flight range of the electricaircraft and at least a battery of the electric aircraft, whereindetermining the most limiting parameter further comprises: generating alist of potential limiting parameters as a function of the comparison,wherein the potential limiting parameters list comprises: an inverterhealth of the electric aircraft, wherein an inverter comprises anelectric component configured to transform direct current to alternatingcurrent; and a parameter related to a flight mode of the electricaircraft, wherein the determined flight mode is a transition flight moderepresenting a transition between a hover flight mode and a fixed-wingflight mode; and determining the most limiting parameter of the electricaircraft as a function of the transition flight mode and inverterhealth, wherein the most limit parameter comprises at least an inverterfailure; generate a mitigating response as function of the most limitingparameter, wherein the mitigation response comprises a switching of aflight mode of the electric aircraft; and communicate the most limitingparameter and the mitigating response to a pilot indicator incommunication with the at least a processor, wherein the pilot indicatoris configured to display the most limiting parameter and the mitigatingresponse to a user.
 2. (canceled)
 3. The apparatus of claim 2, whereinthe flight confidence parameter further comprises data indicatingtolerances for fuel usage of a flight.
 4. The apparatus of claim 1,wherein the at least a processor is further configured to: receivetraining data correlating aircraft data to most limiting parameters;train a most limiting parameter machine learning model with the trainingdata, wherein the most limiting parameter machine learning model isconfigured to input aircraft data and output a most limiting parameter;and determine the most limiting parameter as a function of the mostlimiting parameter machine learning model.
 5. (canceled)
 6. Theapparatus of claim 1, wherein the at least a processor is furtherconfigured to generate a power saving flight plan as a function of themost limiting parameter.
 7. The apparatus of claim 6, wherein the atleast a processor is further configured to display the power savingflight plan through the pilot indicator.
 8. The apparatus of claim 1,wherein the at least a processor is further configured to compare theaircraft data from the sensing device to an operational threshold of theflight confidence parameter.
 9. The apparatus of claim 8, wherein the atleast a processor is further configured to alert a user of at least apart of a flight system of the electric aircraft that exceeds theoperational threshold.
 10. The apparatus of claim 1, wherein the atleast a processor is further configured to automatically adjust at leasta part of a flight system of the electric aircraft as a function of themost limiting parameter.
 11. A method for determining a most limitingparameter in an electric aircraft, comprising: measuring, at a sensingdevice of the electric aircraft, a parameter of the electric aircraft;generating, at the sensing device, aircraft data as a function of theparameter of the electric aircraft; communicating the aircraft data fromthe sensing device to at least a processor and a memory communicativelyconnected to the at least a processor; comparing, by the at least aprocessor, the aircraft data to at least a flight confidence parameterusing an optimization criterion, wherein the optimization criterion isfurther configured to assign weights to at least an attribute of aflight, and wherein the weights are further configured to indicate arelative importance of the at least an attribute of the flight;determining, by the at least a processor, a most limiting parameter ofthe electric aircraft as a function of the aircraft data, wherein themost limiting parameter is related to at least a component of theelectric aircraft with a greatest effect on a flight range of theelectric aircraft and at least a battery of the electric aircraft,wherein determining the most limiting parameter further comprises:generating a list of potential parameters as a function of thecomparison, wherein the potential limiting parameters list comprises: aninverter health of the electric aircraft, wherein an inverter comprisesan electric component configured to transform direct current toalternating current; and a parameter related to a flight mode of theelectric aircraft, wherein the flight mode is a transition flight moderepresenting a transition between a hover flight mode and a fixed-wingflight mode; and determining the most limiting parameter of the electricaircraft as a function of the transition flight mode and inverterhealth, wherein the most limiting parameter comprises at least aninverter failure; generating a mitigating response as function of themost limiting parameter, wherein the mitigation response comprises aswitching of a flight mode of the electric aircraft; and communicatingthe most limiting parameter of the electric aircraft and the mitigatingresponse from the at least a processor to a pilot indicator incommunication with the at least a processor.
 12. The method of claim 11,wherein the pilot indicator is configured to display the most limitingparameter to a user.
 13. The method of claim 11, wherein the flightconfidence parameter further comprises data indicating tolerances forfuel usage of a flight.
 14. The method of claim 11, wherein the at leasta processor is further configured to: receive training data correlatingaircraft data to most limiting parameters; train a most limitingparameter machine learning model with the training data, wherein themost limiting parameter machine learning model is configured to inputaircraft data and output a most limiting parameter; and determine themost limiting parameter as a function of the most limiting parametermachine learning model.
 15. (canceled)
 16. The method of claim 11,wherein the at least a processor is further configured to generate apower saving flight plan as a function of the most limiting parameter.17. The method of claim 16, wherein the at least a processor is furtherconfigured to display the power saving flight plan through the pilotindicator.
 18. The method of claim 11, wherein the at least a processoris further configured to compare the aircraft data from the sensingdevice to an operational threshold of the flight confidence parameter.19. The method of claim 18, wherein the at least a processor is furtherconfigured to alert a user of at least a part of a flight system of theelectric aircraft that exceeds the operational threshold.
 20. The methodof claim 18, wherein the at least a processor is further configured toautomatically adjust at least a part of a flight system of the electricaircraft as a function of the most limiting parameter.
 21. The apparatusof claim 1, wherein the sensing device comprises a gas sensor configuredto detect a vent gas discharged from an electrochemical cell of the atleast a battery.
 22. The method of claim 11, wherein the sensing devicecomprises a gas sensor configured to detect a vent gas discharged froman electrochemical cell of the at least a battery.